{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3e9dc63b",
   "metadata": {},
   "source": [
    "# 时间序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e28c383c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/ar_model.py:791: FutureWarning: \n",
      "statsmodels.tsa.AR has been deprecated in favor of statsmodels.tsa.AutoReg and\n",
      "statsmodels.tsa.SARIMAX.\n",
      "\n",
      "AutoReg adds the ability to specify exogenous variables, include time trends,\n",
      "and add seasonal dummies. The AutoReg API differs from AR since the model is\n",
      "treated as immutable, and so the entire specification including the lag\n",
      "length must be specified when creating the model. This change is too\n",
      "substantial to incorporate into the existing AR api. The function\n",
      "ar_select_order performs lag length selection for AutoReg models.\n",
      "\n",
      "AutoReg only estimates parameters using conditional MLE (OLS). Use SARIMAX to\n",
      "estimate ARX and related models using full MLE via the Kalman Filter.\n",
      "\n",
      "To silence this warning and continue using AR until it is removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.ar_model.AR', FutureWarning)\n",
      "\n",
      "  warnings.warn(AR_DEPRECATION_WARN, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels as sm\n",
    "\n",
    "df=ts.get_k_data('399300',index=True,start='2016-01-01',end='2016-12-31')\n",
    "df.head()\n",
    "df['rtn']=np.log(df['close'])-np.log(df['close'].shift(1))\n",
    "df=df.dropna()\n",
    "\n",
    "rtn = np.array(df['rtn'])\n",
    "model=sm.tsa.api.AR(rtn)\n",
    "fit_AR=model.fit()\n",
    "# plt.figure(figsize=(10,4))\n",
    "# plt.plot(rtn,'b',label='return')\n",
    "# plt.plot(fit_AR.fittedvalues,'r',label='AR fit')\n",
    "# plt.legend()\n",
    "\n",
    "pi,sin,cos=np.pi,np.sin,np.cos\n",
    "r1=1\n",
    "theta=np.linspace(0,3*pi,360)\n",
    "x1=r1*cos(theta)\n",
    "y1=r1*sin(theta)\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(x1,y1,'k')\n",
    "roots=1/fit_AR.roots\n",
    "for i in range(len(roots)):\n",
    "    plt.plot(roots[i].real,roots[i].imag,'.r',markersize=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d3d47b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "[[ 1.00000000e+00 -1.78514448e-01  7.83977745e+00  5.11088985e-03]\n",
      " [ 2.00000000e+00  1.56325702e-01  1.38767067e+01  9.69865288e-04]\n",
      " [ 3.00000000e+00 -9.92710340e-02  1.63212977e+01  9.74317656e-04]\n",
      " [ 4.00000000e+00  5.63852755e-02  1.71132625e+01  1.83741428e-03]\n",
      " [ 5.00000000e+00 -1.66989823e-01  2.40887636e+01  2.08749219e-04]\n",
      " [ 6.00000000e+00 -2.66265505e-02  2.42668594e+01  4.66396531e-04]\n",
      " [ 7.00000000e+00  2.09022914e-03  2.42679616e+01  1.02212220e-03]\n",
      " [ 8.00000000e+00  2.63144098e-04  2.42679791e+01  2.06622992e-03]\n",
      " [ 9.00000000e+00  1.16594706e-01  2.77266893e+01  1.05948298e-03]\n",
      " [ 1.00000000e+01 -6.23254206e-03  2.77366147e+01  1.98902948e-03]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/stattools.py:667: FutureWarning: fft=True will become the default after the release of the 0.12 release of statsmodels. To suppress this warning, explicitly set fft=False.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "import tushare as ts\n",
    "import numpy as np\n",
    "\n",
    "df=ts.get_k_data('399300',index='True',start='2016-01-01',end='2016-12-31')\n",
    "df=df.set_index('date')\n",
    "df['rtn']=np.log(df['close'])-np.log(df['close'].shift(1))\n",
    "df=df.dropna()\n",
    "m=10\n",
    "acf,q,p=sm.tsa.acf(df['rtn'],nlags=m,qstat=True)\n",
    "out=np.c_[np.arange(1,11),acf[1:],q,p]\n",
    "output=pd.DataFrame(out,columns=['lag','AC','Q','P-value'])\n",
    "output=output.set_index('lag')\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1d21d7c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "df=ts.get_k_data('399300',index=True,start='2016-01-01',end='2016-12-31')\n",
    "df.head()\n",
    "df['rtn']=np.log(df['close'])-np.log(df['close'].shift(1))\n",
    "df=df.dropna()\n",
    "# print(df)\n",
    "# plt.plot(df['date'],df['rtn'])\n",
    "# plt.plot(df['date'],df['rtn'])\n",
    "# plt.title('rtn')\n",
    "# plt.show()\n",
    "# plt.plot(df['date'],df['rtn'])\n",
    "# plt.plot(df['date'],df['close'])\n",
    "# plt.title('close')\n",
    "# plt.show()\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax1 = fig.add_subplot(111)\n",
    "fig = sm.graphics.tsa.plot_acf(df['rtn'],ax=ax1,lags=50)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "284px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
